Object segmentation processes images containing objects of interest and determines the regions in the images corresponding to those objects of interest. Object segmentation is the key to follow on processing in any imaging applications. If segmented object regions are incorrect. The measurements performed on the segmented objects will certainly be incorrect and therefore any analysis and conclusion drawn based on the incorrect measurements will be erroneous and compromised.
It is difficult to specify what constitutes an object of interest and define the specific segmentation procedures. General segmentation procedures tend to obey the following rules:                Regions of object segmentation should be uniform and homogeneous with respect to some characteristic, such as gray level or texture.        Region interiors should be simple and without many small holes.        Adjacent regions of different objects should have significantly different values with respect to the characteristic on which they are uniform.        Boundaries of each segment should be simple, not ragged, and must be spatially accurate.        
Enforcing the above rules is difficult because strictly uniform and homogeneous regions are typically full of small holes and have ragged boundaries. Insisting that adjacent regions have large differences in values could cause regions to merge and boundaries to be lost. Therefore, it is very difficult to create a universal object segmentation method that will work on all types of objects.
It is highly desirable to create a general-purpose object segmentation method that can be trained to automatically differentiate different types of objects of interest. The differentiated objects may not have clear intensity differences or high edge contrast in the particular image analysis application.